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Other AI Companies Should Be Very Afraid of What Just Happened to Character.AI, According to Its Own Bankruptcy Documents: Be afraid. (futurism.com)
submitted 11 hours ago by to c/technology@lemmy.world
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[–] 25 points 4 hours ago (2 children)

When I first heard about this bankruptcy, one of my first thoughts was, here it goes. This is only the very beginning of the landslide though, so it's going to take a while before it reaches the bottom, and it's going to pick up a lot more debris on the way.

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  • [–] 19 points 4 hours ago (1 child)

    Or, as the article optimisitically puts it:

    The fate of Character.AI, the controversial AI companion platform, may come to stand as a warning.

    Wrong. Character.AI may come to be seen as the first car in the train to derail.

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    • [–] 58 points 6 hours ago (2 children)

    “We lose money on every sale, but we make up for it in volume!”

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  • [–] 9 points 2 hours ago (1 child)

    I can't imagine how much money Google is losing right now. They do 8.5 to 13.7 billion searches a day (+1 because I just googled that). By forcing LLM first, they've got to be burning cash.

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    • [–] 41 points 8 hours ago* (18 children)

    Anthropic made a smart business decision to mostly focus on enterprise customers rather than regular consumers. Only around 10% of their revenue comes from their heavily subsidized subscription plans. The remaining 90%ish comes from API and enterprise usage billed at per-token rates, which is something that's actually profitable for them. They have a decent margin on token rates. It's R&D, hardware, and subscriptions that are putting them in the red at the moment.

    I suspect they'll discontinue subscriptions some time after their IPO. Even though subscriptions are only responsible for 10% of revenue, they consume 42% of their inference compute power (https://www.theregister.com/ai-and-ml/2026/10/06/anthropic-claude-subscription-plan-provides-more-value-than-openais-study-says/5301470)

    In comparison, around 70% of OpenAI's revenue comes from subscriptions. Without subsidized subscriptions, they'd lose most of their customers.

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  • [–] 40 points 6 hours ago

    It's R&D, hardware

    "Yeah, besides those integral parts of our costs, we're profitable"

    😅.

    It's like a hair dresser saying "If I don't count the rent of my shop and all the supplies, I'm profitable".

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  • [–] 7 points 4 hours ago (2 children)

    Those enterprise customers are companies like this very one, who then have to heavily discount their rates or nobody wants it, (and as we see here, even then) so that's not a solution for anthropic.

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  • [–] 1 point 1 hour ago (1 child)

    I don't have data to back this up but I'd say thay the majority of their customers aren't also selling AI services themselves. It's customers that use AI for their business processes, software development, etc.

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  • [–] 1 point 3 minutes ago*

    Nobody has extremely good data about this because it's in their interest to make that as opaque as possible, but the reporting I've seen suggests that it is, if not the majority at least an extremely large fraction of their customers which are selling AI services, and even where it's used for in-house software development that is largely bankrolled by VC hoping to gain a competitive advantage with AI. It doesn't really appear that the API pricing offers better cost/productivity than just hiring junior devs (lol).

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  • [–] 27 points 7 hours ago (4 children)

    Anthropics numbers are probably better than OpenAIs, but i am willing to eat my boots (with ketchup and mustard tho) if they are even close to profitable at API pricing.

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  • [–] 5 points 4 hours ago (1 child)

    It's subsidized. I will eat my hat if the number is less than 50%. It is even crazier at work. My personal account cost 3 times as much for the same prompt on my work account. Inference cost is the same for all users, you can't mega-ultra scale something that is at maximum scale labeled as a hyper scale.

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  • [–] 5 points 4 hours ago*

    Yeah, subsidized by mainly Nvidias circular financing crap. This will cause a major recession, and it is so obvious for everyone looking at how much cash investors are throwing at this thing and how much you can actually earn with something that confidently misinforms you on the regular. Roleplaying like Character.ai is one of the better uses, because there false information isn't that relevant. Then there's coding where it is easy to automatically check for errors, and then... scientific uses. But i think that's it, and you can't prop up all that investment just with coding, RP and weather analysis (which is woke by definition in the US to boot) or interpreting CT scans.

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  • [–] 6 points 4 hours ago (1 child)

    They are (maybe, allegedly) "profitable on inference" i.e. the API pricing is higher than the actual compute they use just for the input/output tokens for the query. That excludes, though, training, capex, staff, etc., which... is by far the vast majority of their actual expenses.

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  • [–] 5 points 4 hours ago

    At least according to Ed Zitron, they only appeared profitable due to creative bookkeeping. They timed the announcement of their shitty "annualized revenue" to coincide with the few months of vastly reduced rent at the datacenter Musk rented to them as well as the shift to token based billing for enterprise customers. Their inference is likely not profitable.

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  • [–] 5 points 5 hours ago* (last edited 4 hours ago) (2 children)

    I’m surprised how easy it is to blow through hundreds of dollars in Claude tokens at work. They certainly aren’t subsidizing anything. If the Qwen 3.8 27B model I run on my gaming PC at home were priced like Claude, I’d probably have a $1000 a month bill.

    Edit:

    I bought a 7900 XTX a couple months ago for less than 2 months worth of Claude bills at work that runs 3.8 27B quite well at 200k context that is pretty on par with Sonnet 5.

    Maybe Anthropic isn’t bleeding money as fast as they would otherwise be, but their premium pricing is pretty hard to justify. If Qwen 4 27B keeps pace with Sonnet 5.5, then we’ll basically have a Fable 5.1 class model that runs on a gaming PC.

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  • [–] 1 point 7 minutes ago

    Don't know if it was you but someone pointed out a few months ago that the 7900 was the last 24GB card to be found for less than $1k and I grabbed one as an upgrade from a 3070. Running local models it's less reliable than Claude at work, but not by much, and it was a good upgrade for gaming.

    Before the return period was up it had gone from ~$900 to past $1.2k and I wasn't eligible for a repair/replacement just a refund, the market is completely sideways.

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  • [–] 4 points 3 hours ago

    I’m surprised how easy it is to blow through hundreds of dollars in Claude tokens at work. They certainly aren’t subsidizing anything.

    That is the subsidized rate!

    It costs far more for all the training and compute costs together than what you are currently paying.

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  • [–] 14 points 8 hours ago

    Yeah I have the $200/m sub for work and I’m using about $200/day volume of tokens at API rate. Obviously this isn’t a sustainable business model.

    Am I prepared to pay full rate? Not yet, but if models keep improving at the rate they are then probably at some point. Opus 5.5 has been a huge increase in quality at a cheaper cost to deliver a task.

    With next gen gpus and better models reducing the ‘cost per task’ I think they’ll get there.

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    [–] 16 points 7 hours ago

    Let's hope the rest follows.

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    [–] 25 points 8 hours ago (1 child)

    AI has two, and only two, paths to monetization. Users pay the server costs, or advertisers.

    Not enough users are interested in paying for something they can't trust to be accurate, and I suspect/hope no one will use these tools once they start spewing advertisements from their training data.

    Could some of these tools survive on enterprise users alone? Maybe, but businesses are taking on a lot of legal risks when they make the confident guesser bot a key part of their supply chain.

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    • [–] [S] 80 points 11 hours ago (40 children)

    Character.AI discovered the one conversation its bots couldn’t keep going: the one with its accountants. $56 million in compute costs in eight months, and the monetization strategy was apparently: hope the next investor is even lonelier than our users. Silicon Valley keeps confusing engagement with a business model.

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  • [–] 69 points 11 hours ago* (39 children)

    If LLMs can't be made more power efficient, then the technology isn't ready yet for general use. There may have to be fundamental engineering breakthroughs before what these systems do can be more than a very expensive and inefficient worldwide proof of concept.

    Of course, the military and the surveillance industry will still want to keep it, even if no one else can afford it, and we'll be the ones paying for it.

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  • [–] 41 points 10 hours ago (21 children)

    They CAN be made more efficient. Hell, AMD just bought a company that translated LLMs to direct silicon... which should enable scalability if it wasn't for one of the key players owning the tech thus limiting its use.

    It's also estimated that some 70% of Claude tokens gets spent on recursive self-correction so the output is higher quality. Instead of bettering the model, Anthropic just keeps throwing money at it to make the problem go away.

    Meanwhile Chinese models - as small as 36-40-80b params - are outdoing these fancy frontier models if the right harness is used.

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  • [–] 28 points 10 hours ago (16 children)

    I've spent the last month or so using nothing but a local 27B Qwen model as my coding assistant and it's working fine. The big American AI companies could all collapse in ruins tomorrow and it's not going to slow things down all that much, anyone who wants to use AI will be able to continue using it. Might even be better in the long run if that happens - the people who are scared of ASI will get their much-demanded temporary slowdown at the frontier while everyone else catches up with open-weight models that everyone can use without central control.

    Pity about the economy in the meantime, I guess.

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  • [–] 21 points 9 hours ago (7 children)

    anyone who wants to use AI will be able to continue using it

    Anyone who's privileged enough to have the hardware.

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  • [–] 7 points 5 hours ago (1 child)

    The kind of specs that would enable local LLM usage were available on laptops since 2020. The floor for acceptable performance for LLMs stopped literally just before 24+gb of VRAM would have become pretty common in consumer applications. The reason this technology isn’t more ubiquitously available is because ‘they’ thought it would be more profitable to hoover up literally every byte of RAM and GPU in the world rather than let the technology continue to naturally evolve to a place where any consumer could have run useful models on their home computers. In other words, your anger is misplaced.

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  • [–] 10 points 9 hours ago

    Open models will still be available via API from third-party hosts. The bigger ones like Kimi K3 or GLM 5.3 don't fit on conventional desktop computers to begin with, most non-institutional users of those would already be doing that (eg via OpenRouter).

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  • [–] 1 point 4 hours ago (3 children)

    What kind of hardware do you need to run a model like that? And what do you use :-) ?

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  • [–] 4 points 3 hours ago (2 children)

    I run a 5 bit quantized Qwen 3.8 27b at home on a gaming PC with an RX 7900 XTX and RAG memory using Gemma MoE (i forget what one)running on an old 1080 ti on my Valheim server.

    It takes 3-15 minutes to run depending on how many tool calls the model makes and that's mostly memory or web scrapes since that runs on the slower hardware. I mostly just use it as a discord bot to look stuff up for me and to fetch the news.

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  • [–] 2 points 2 hours ago (1 child)

    Sabata, goddess of computing, can I run it on a 32GB system with a 4GB nVidia GPU?

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  • [–] 3 points 1 hour ago

    Probably not, or if it runs it may take hours or days to get a full output. VRAM on the GPU really decides what will run reasonably. Try an 8 billion parameter phone sized model and see if its faster than sending a letter, then keep moving up until your setup can't handle it.

    8B models are a bit too dumb to use any tools reliably but should handle a basic conversation.
    llama cpp will allow you to manually load a model and tweak settings.

    unsloth will give you a basic setup with some tools, manage model downloads, and settings.

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    • [–] 4 points 8 hours ago (1 child)

    Didn't all of their devs get poached by Google after being bought out?

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